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May 6, 2011

Using historical data to justify BI investments – Part I

The earliest recorded surd

This is the first of a two part piece. In the initial chapter, I provide some background on Insurance industry concepts and practices. These will be built on in the forthcoming second chapter, in which I will explain how I have used historical data to justify BI investments.

Readers who are already au fait with insurance may choose to wait for the next instalment.
 
 
Introduction

Quite some time ago, when I wrote Measuring the Benefits of Business Intelligence, I mentioned that, in some circumstances, I had been able to leverage historical data (is there any other kind?) to justify Business Intelligence investments. I briefly touched on this area in my recent interview with Microsoft’s Bruno Aziza (@brunoaziza) and thought that it was well past time me writing more fully on the topic.

My general approach applies where there are periodic decisions to be made about a business relationship and where how that relationship has performed in the past informs these decisions. These criteria particularly pertain to the industry in which I ran my first BI / DW project; commercial property and casualty insurance. While I hope that users from other sectors may be able to extrapolate my example to apply to them, it is to insurance that I will turn to explain what I did.
 
 
An insurance primer

I have always wanted to launch a '[...] for Pacifiers' series in the US

My previous article, The Specific Benefits of Business Intelligence in Insurance, starts with a widely used and pig-related (no typo) explanation of how insurance works, both for the insurer and the insured. I won’t repeat this here, but if you are unfamiliar with the area I recommend you taking a look first.

Although of course there are exceptions (event related insurance for example), many commercial insurance policies – just like those that most of us purchase in our personal lives to cover cars and property – have an annual term after which either party can decide whether or not to renew the cover. At renewal, as in the pig example, the insurer will first of all want to assess whether or not they have received more money than they have paid out over the past year. However, the entire point of insurance is that sometimes an event occurs which requires the insurer to give the insured a sum in excess of the premium that they have paid in a given year (or indeed over many years). The insurer is therefore less interested in whether a particular year has been bad – from their perspective – than whether the overall relationship has been, or will become, bad. Perhaps I am over simplifying, but if in most years the insurer pays out less in settling claims than they receive in premium (or ideally there are no claims at all) and if one bad year’s claims are unlikely to negate the benefits accrued in the normal years, then this is good business for the insurer.
 
 
Some rational comments

The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift

I have bandied about a number of rather woolly concepts in the previous section which include: how much money the insured has paid out and how much they have taken in. Of course these things tend to be more complicated. On the simpler side of the equation, broadly speaking, money coming in is from the insurance premiums paid by customers (but see also the box appearing below).

Investment income

Some insurers are actually relatively relaxed about paying out more in claims that they receive in premium over the life of a policy. This is because of timing differences. So long as the claims are settled some time after premium is received and so long as there are relatively lucrative investment opportunities (remember that?), it may be that the investment income that the insurer can generate while it has use of the insured’s premium will more than compensate for what might be termed an operating loss on the policy. Equally some insurers will have the business goal of – at least in aggregate – always having premiums exceeding claims and thus making a profit on their core underwriting activities. In this case any investment income is added to the underwriting-related profits, rather than compensating for underwriting-related losses. I won’t complicate this article any further by including investment income, but it is a factor in the profitability of insurance companies.

Equally broadly speaking, money going out is normally in six categories:

  1. settlement of claims – often referred to as case payments
  2. claims adjusters’ estimates for the settlement of specific claims that have been notified to the insurer, but not as yet paid – often referred to as case reserves
  3. actuarial estimates of insurance events that have occurred, but which have not yet been reported to the insurer – generally known as incurred but not reported losses, or IBNR (more on this later)
  4. fees paid to insurance intermediaries for placing their clients’ business with the carrier – commission
  5. premiums paid to other organisations to transfer some of the risk associated with specific policies, or baskets of types of policies – facultative or treaty reinsurance
  6. the general expense of being in business (staff, premises, consumables, equipment, IT, advertising, uncollectable premiums etc.)

In the cause of clarity, I will lump commission, reinsurance and the general expense of being in business into Other Expenses for what follows. However please bear in mind that, as is often the case in life, things are not as simple as I will make them out to be.

Rather than dealing in monetary units, insurance companies like percentages; though they then insist on referring to these as ratios. Taking the above categories of money flowing in and out of an insurance company, the main ratios that they consider are then:

Ratio Calculation
 
Paid Loss =
Claims Paid
Premium
 
Reported Loss =
Claims Paid + Case Reserves
Premium
 
Incurred Loss =
Claims Paid + Case Reserves + IBNR
Premium
 
Expense =
Other Expenses
Premium
 
Combined =
Claims Paid + Case Reserves + IBNR + Other Expenses
Premium

 
 
Incurred but not reported

Not sure whether the Nixon administration set up any Watergate-related reserves

This concept requires a short diversion as later on I will exclude it from our discussions and will need to explain why. There are some interesting time lags in insurance. Take the sad case of asbestosis (also mentioned in my previous article). Here those unfortunately exposed developed symptoms of the disease in some cases many years later. However if their exposure was in say 1972, they would be covered by whatever Employers Liability policy their organisation held or whatever personal policy they held in the case of the self-employed. An asbestosis sufferer may have changed insurance company ten times since their exposure, but it is the insurance company who provided cover at the time who is liable for any claims.

Rather than waiting for such claims to emerge, insurance companies follow the best practise of recognising liabilities at the earliest point. Because of this, they set up estimated reserves for claims that they may receive in future years (or decades) and apply these to the year in which the policy was in force. Of course in some lines of business, say Property cover, most claims are reported as soon as they occur and so IBNR reserves are low. However in others, say Directors and Officers Liability, or the Employers Liability mentioned above, claims may arise many years hence and IBNR can be a big factor in results.

It should be stressed that IBNR is seldom calculated for a single policy (though it is conceivable that this would happen on a very large risk). Instead it is estimated for classes of policies, often grouped into lines of business, and the same “rate” of IBNR is applied across the board. Of course IBNR is calculated based on experience of losses in the same baskets of policies in previous years, adjusted to take account of current differences (e.g. more or less favourable economic conditions for Directors and Officers Liability, or maybe rising or falling property indeces for Property).

For reasons that are probably obvious, lines of business where most claims are promptly reported (i.e. low IBNR) are called short-tail lines. Those where claims may emerge some time after the period covered by the policy (i.e. high IBNR) are called long-tail lines. Later on I will be focussing just on short-tail business.

[Incidentally, improving this process of estimation is one of the specific benefits of Business Intelligence in insurance that I highlighted in my previous article.]
 
 
Underwriting Year

Fundamental particles of the Underwriting Year

Something else may have occurred to readers when considering the time lags that I reference in the previous section, namely that while a policy may last from say 1st January 2006 to 31st December 2006, claims against this may occur either during this period, or after it. The financial statements of an insurance company will place claims in the period that they are notified or settled. So in the above example, a claim paid on 23rd April 2008 (assuming the financial and calendar years coincide) will be reflected in the 2008 report and accounts.

However it is often useful for analysis purposes to lump together all of the claims relating to a policy and associate these with the year in which it was written. Again in our example this would mean our 23rd April 2008 claim would be recorded in the Underwriting Year of 2006. So an Underwriting Year report comparing 2006 and 2007 say would have the premium for all policies written in 2006 and all the claims against these policies – regardless of when they occur – compared to the premium for 2007 and all the claims against these policies, whenever they occur.

Because of this, Underwriting Year reports provide a good measure of the performance of policies (or books of business) over time, regardless of how associated losses are dispersed. By contrast Calendar Year (i.e. financial) reports will often have premium from policies written in say 2010 combined with losses from policies written in say 2000 – 2010.
 
 
Tune in next time…

BBC ANNOUNCER: Tune in to the next exciting instalment of... CAST: Dick Barton, Special Agent!

Having laid some foundations, in the follow-on article, which is yet to be published, I will draw on the various concepts that I have introduced above and explain how I applied them to justify a major, multi-year Business Intelligence / Data Warehousing programme within the insurance industry.
 


Filed under: business intelligence, data warehousing Tagged: ibnr, insurance, loss ratio, underwriting year

Posted by Peter Thomas at 6:32 PM

April 18, 2011

Trouble at the top

IRM MDM/DG

Several weeks back now, I presented at IRM’s collocated European Master Data Management Summit and Data Governance Conference. This was my second IRM event, having also spoken at their European Data Warehouse and Business Intelligence Conference back in 2010. The conference was impeccably arranged and the range of speakers was both impressive and interesting. However, as always happens to me, my ability to attend meetings was curtailed by both work commitments and my own preparations. One of these years I will go to all the days of a seminar and listen to a wider variety of speakers.

Anyway, my talk – entitled Making Business Intelligence an Integral part of your Data Quality Programme – was based on themes I had introduced in Using BI to drive improvements in data quality and developed in Who should be accountable for data quality?. It centred on the four-pillar framework that I introduced in the latter article (yes I do have a fetish for four-pillar frameworks as per):

The four pillars of improved data quality

Given my lack of exposure to the event as a whole, I will restrict myself to writing about a comment that came up in the question section of my slot. As per my article on presenting in public, I try to always allow time at the end for questions as this can often be the most interesting part of the talk; for delegates and for me. My IRM slot was 45 minutes this time round, so I turned things over to the audience after speaking for half-an-hour.

There were a number of good questions and I did my best to answer them, based on past experience of both what had worked and what had been less successful. However, one comment stuck in my mind. For obvious reasons, I will not identify either the delegate, or the organisation that she worked for; but I also had a brief follow-up conversation with her afterwards.

She explained that her organisation had in place a formal data governance process and that a lot of time and effort had been put into communicating with the people who actually entered data. In common with my first pillar, this had focused on educating people as to the importance of data quality and how this fed into the organisation’s objectives; a textbook example of how to do things, on which the lady in question should be congratulated. However, she also faced an issue; one that is probably more common than any of us information professionals would care to admit. Her problem was not at the bottom, or in the middle of her organisation, but at the top.

So how many miles per gallon do you get out of that?

In particular, though data governance and a thorough and consistent approach to both the entry of data and transformation of this to information were all embedded into the organisation; this did not prevent the leaders of each division having their own people take the resulting information, load it into Excel and “improve” it by “adjusting anomalies”, “smoothing out variations”, “allowing for the impact of exceptional items”, “better reflecting the opinions of field operatives” and the whole panoply of euphemisms for changing figures so that they tell a more convenient story.

In one sense this was rather depressing, someone having got so much right, but still facing challenges. However, it also chimes with another theme that I have stressed many times under the banner of cultural transformation; it is crucially important than any information initiative either has, or works assiduously to establish, the active support of all echelons of the organisation. In some of my most successful BI/DW work, I have had the benefit of the direct support of the CEO. Equally, it is is very important to ensure that the highest levels of your organisation buy in before commencing on a stepped-change to its information capabilities.

I am way overdue employing another sporting analogy - odd however how must of my rugby-related ones tend to be non-explicit

My experience is that enhanced information can have enormous payback. But it is risky to embark on an information programme without this being explicitly recognised by the senior management team. If you avoid laying this important foundation, then this is simply storing up trouble for the future. The best BI/DW projects are totally aligned with the strategic goals of the organisation. Given this, explaining their objectives and soliciting executive support should be all the easier. This is something that I would encourage my fellow information professionals to seek without exception.
 


Filed under: business intelligence, change management, cultural transformation, data quality Tagged: IRM UK, seminars

Posted by Peter Thomas at 4:00 AM

April 10, 2011

Data visualisation

Some pictures speak for themselves:

If you don't know what this is, check out the announcement from the CDF Collaboration at: http://www.fnal.gov/pub/today/archive_2011/today11-04-07_CDFpeakresult.html - All you have to do is click here. HINT: the peak at 140 GeV/c^2 may be important.
 


Filed under: general Tagged: fermilab, particle physics, tevatron

Posted by Peter Thomas at 4:28 PM

The triangle paradox – solved

When I posted The triangle paradox, I said that I would post a solution in few days. As per the comments on my earlier article, some via Twitter and indeed the context of the article in which this supposed mathematical conundrum was posted, the heart of the matter is an optical illusion.

If we consider just the first part of the paradox:

More than meets the eyes

Then the key is in realising that the red and green triangles are not similar (in the geometric sense of the word). In particular the left hand angles are not the same, thus when lined-up they do not form the hypotenuse of the larger, compound triangle that our eyes see. In the example above, the line tracing the red and green triangles dips below what would be the hypotenuse of the big triangle. In the rearranged version, it bulges above. This is where the extra white square comes from.

It is probably easier to see this diagrammatically. The following figure has been distorted to make things easier to understand:

Dimensions exaggerated

Let’s start with my point about the triangles not being similar:

EAB = tan-1(2/5) ≈ 21.8°

FAC = tan-1(3/8) ≈ 20.6°

So the two triangles are not similar and, as stated above, the two arrangements don’t quite line up to form the big triangle shown in the paradox. There is a ”gap” between them formed by the grey parallelogram above, whose size has been exaggerated. This difference gets lost in the thickness of the lines and also our eyes just assume that the two arrangements form the same big triangle.

To work out the area of the parallelogram:

AE = (22 + 52)½ = √29
EI = (32 + 82)½ = √73
AI = (52 + 132)½ = √194

The area of a triangle with sides a, b and c is given by:

Area of triangle

Sparing you the arithmetic, when you substritute the values for AE, EI and AI in the above equation, the area of ∆ AEI is precisely ½.

∆ AEI and ∆ AFI are clearly identical, so the area of parallelogram AEIF is twice the area of either is

2 x ½ = 1

This is where the ”missing” square comes from.
 


 
As was pointed out in a comment on the original post, the above should form something of a warning to those who place wholly uncritical faith in data visualisation. Much like statistics, while this is a powerful tool in the hands of the expert, it can mislead if used without due care and attention.
 


Filed under: general Tagged: data visualisation, geometry, mathematics, optical illusion, paradox

Posted by Peter Thomas at 9:48 AM

April 8, 2011

Illuminating the darkness

Recrudescence

My partner was kind enough to buy me an Amazon Kindle for Christmas and I have enjoyed using it. Yes there were the problems with them registering me to Amazon.com, rather than Amazon.co.uk (thereby incurring foreign transaction charges). And yes they didn’t cancel a trial Economist subscription I took out on the former when I was transferred to the latter. However, these issues were sorted out and money refunded.

I suppose I had the same initial reaction as many people; that they had left a sticker covering the screen, which was intended to demonstrate what the display looked like. After failing to peal it off (thankfully not too energetically) I realised that the screen was actually that clear and that different from a “normal” computer display (I was thinking smart ‘phone or laptop). I am writing this post on one of my many laptops, the screen is OK, but the Kindle is much easier on the eye and pretty close to a high-quality printed page. Suffice it to say that I downloaded new copies of several of my favourite books to it with the prospect of re-engaging with them at my leisure.

But enough of me singing the general praises of the device, I have discovered a particular benefit. While this may well be realised by other people, it is of particular pertinence to devotees of the works of Joseph Conrad.

Joseph Conrad

As one of the undisputed giants of English prose, it is rather ironic that English itself was either Conrad’s fifth, or sixth, language (chronologically: Polish; Russian – though he later, perhaps understandably given the turbulence of the times, repudiated this as a language; French; Latin; German; and – finally, when he was in his twenties, English). I have greatly appreciated his work, since first reading Heart of Darkness. I won’t attempt to offer a literary appreciation of his genius and leave this to others with greater talents in that area. However, despite coming late to the English tongue, Conrad was a master of it and had an amazing vocabulary.

An indispensable companion to Conrad's works

I generally view myself as being reasonably erudite (less charitably I have been accused of having swallowed a thesaurus), but used to have to keep a dictionary at hand when reading Conrad; either that or try to impute meaning from context (probably getting it wrong more times that I care to admit). In some ways, my own limitations slightly diluted my enjoyment of reading. It is a bit distracting to put down one book, pick up a dictionary, look up a word and then revert to the original tome (it was even more complicated as a child reading Jules Verne’s 20,000 Leagues under the Sea with both a dictionary and gazetteer to hand!).

Incidentally my fondness of Conrad led to my one contribution to the field of science. I established my result after extensive fieldwork involving Nostromo and a daily commute. Thomas’ Theorem is as follows:

While this feat is more than achievable with the works of other authors, it is impossible to read Conrad on the Tube.

However, the Kindle is a joy in this respect as you can look up words using the built in dictionary, quickly, easily and without disturbing the thread of the narrative too much. This has got me out of my rather lazy habit of assuming that I sort of know what a word means and thereby given me a few surprises. Based on the the initial illustration above, for example, I had to modify my understanding of recrudescence!

Of course this means that I may have to re-evaluate whether Thomas’ Theorem holds in all conditions. Perhaps a sub-clause excluding the use of a Kindle is required. I will report back…
 


 
This is not the first time that Conrad has appeared in the pages of this blog, I had the temerity to also reference him in Aphorism of the Week some time ago.
 


Filed under: technology Tagged: amazon, dictionary, joseph conrad, kindle, literature, vocabulary

Posted by Peter Thomas at 2:55 PM

April 7, 2011

What is wrong with this picture?

Following on from the optical illusions that I featured earlier in the week, here is another picture with something subtly (or perhaps not so subtly) wrong with it. Can you spot what?

So which one is your favourite?
 


Filed under: social media Tagged: social bookmarking

Posted by Peter Thomas at 6:00 AM

April 4, 2011

The triangle paradox

This seems to be turning into Mathematics week at peterjamesthomas.com. The “paradox” shown in the latter part of this article was presented to the author and some of his work colleagues at a recent seminar. It kept company with some well-know trompe l’œil such as:

Old or young woman?

and

Quadruped?

and

Parallel lines?

However the final item presented was rather more worrying as it seemed to be less related to the human eye’s (or perhaps more accurately the human brain’s) ability to discern shape from minimal cues and more to do with mathematical fallacy. The person presenting these images (actually they were slightly different ones, I have simplified the problem) claimed that they themselves had no idea about the solution.

Consider the following two triangles:

Spot the difference...

The upper one has been decomposed into two smaller triangles – one red, one green – a blue rectangle and a series of purple squares.

These shapes have then been rearranged to form the lower triangle. But something is going wrong here. Where has the additional white square come from?

Without even making recourse to Gdel, surely this result stabs at the heart of Mathematics. What is going on?

After a bit of thought and going down at least one blind alley, I managed to work this one out (and thereby save Mathematics single-handedly). I’ll publish the solution in a later article. Until then, any suggestions are welcome.
 


Filed under: general Tagged: mathematics, paradox

Posted by Peter Thomas at 7:22 PM

April 3, 2011

Half full, or half empty?

Glass half, er...

Someone being described as a “glass half-full” or “glass half-empty” sort of person is something that one hears increasingly frequently. I was recently discussing this with a friend and we both agreed that the analogy was unhelpful. First it supports a drastically simplistic and binary view of people having fixed attitudes and behaviours in all circumstances. Day-to-day observation suggests on the contrary that a person my be an avid optimist one day about one thing and a manic pessimist the next day about another thing. This rather shallow type of characterisation rather reminds me of some of the subjects I touched on in Pigeonholing – A tragedy some time ago.

However, there is a more fundamental consideration; wilful inaccuracy. A glass that is half empty is also half full; that’s the definition of a half. Either description is 100% valid and therefore logically can tell you nothing about the person’s mindset.

Instead what might be more apposite is to adopt a different way to divide sheep from goats. This is still rather too binary for my taste, but at least it has the merit of a greater degree of rigour. I propose dividing people according to how they view a glass that is three quarters empty:

  • I still have some left: optimist
  • There isn’t very much left: pessimist

I think that all of our lives would be much the better for adopting this simple principle.

The International Organisation for stamping out sloppiness in spoken speech

Accordingly, I am going to submit this recommendation to the International Standards Organisation for their urgent consideration. I’ll make sure that I keep readers up-to-date with how my submission progresses.
 


Filed under: general Tagged: calculus, iso, linguistics, mathematics, stereotypes

Posted by Peter Thomas at 5:11 PM

March 27, 2011

An informed decision

Caterham 7 vs Data Warehouse appliance - spot the difference

A friend and fellow information professional is currently responsible for both building a new data warehouse and supporting its predecessor, which is based on a different technology platform. In these times of ever-increasing focus on costs, she had been asked to port the old warehouse to the new platform, thereby avoiding some licensing payments. She asked me what I thought about this idea and we chatted for a while. For some reason, our conversation went off at a bit of a tangent and I started to tell her the story of an acquaintance of mine and his recent sad experiences.

+++

My acquaintance, let’s call him Jim to avoid causing any embarassment, had always been interested in cars; driving them, maintaining them, souping them up, endlessly reading car magazines and so on. His dream had always been to build his own car and his eye had always been on a Caterham kit. I suppose for him the pleasure of making a car was at least as great, if not more, as the pleasure of driving one.

It's just like Lego

Jim saved his pennies and eventually got together enough cash to embark on his dream project. Having invested his money, he started to also invest his time and effort. However, after a few weeks of toil, he hit a snag. It was nothing to do with his slowly emerging Caterham, but to do with the more quotidian car he used for his daily commute to work. Its engine had developed a couple of niggles that had been resistant to his own attempts to fix them and he had reluctantly decided that it was in need of some new parts and quite expensive ones at that. Jim had already spent quite a bit of cash on the Caterham and more on some new tools that he needed to assemble it. The last thing he wanted to do now was to have a major outlay on his old car; particularly because, once the Caterham was finished, he had planned to trade it for its scrap-metal worth.

But now things got worse, Jim’s current car failed its MOT (vehicle safety inspection for any non-UK readers) because the faulty engine did not meet emission standards. However, one of his friends came up with a potential solution. He said, ”As you have already assembled the Caterham engine, why not put this into your current car and use this instead? You can then swap it out into the Caterham chassis and body when you have built this.”

Headless Jim - with cropped face to protect his anonymity

This sounded like a great idea to Jim, but there were some issues with it. His Cateham was supplied with a Cosworth-developed 2.3-litre Ford Duratec engine. This four-cylinder twin cam unit was the wrong size and shape to fit into the cavity left by removing the worn-out engine from his commuting car. Well as I had mentioned at the start, Jim was a pretty competent amateur mechanic and he thought that he had a good chance of rising to the challenge. He was motivated by the thought of not having to shell out extra cash and in any case he loved tinkering with cars.

So he put in some new brackets to hold the Caterham engine. He then had to grind-down a couple of protruding pieces of the Duratec block to gain the extra 5 mm necessary to squeeze it in. The fuel feeds were in the wrong place, but a bit of plumbing and that was also sorted. Perhaps this might cause an issue with efficiency of the engine burn cycle, but Jim figured that it would probably be OK. Next the vibration dampers were not really up to the job of dealing with the more powerful engine and neither was the exhaust system. No worries, thought Jim, a tap of a hammer here, a bend of a pipe here and he could also add in a couple of components that had been sitting at the back of his garage rusting for years as well. Eventually everything seemed fine.

Jim ventured out of his garage in his old car, with its new engine. He was initially a bit trepidatious, but his work seemed to be hanging together. Sure the car was making a bit of a noise, shaking a bit and the oil temperature seems a bit high, but Jim felt that these were only minor problems. He told himself that all his handiwork had to do was to hang together for a few more months until he finished the rest of the Caterham.

Angular momentum = Sum over i : Ri x mi x Vi

With these nice thoughts in mind, Jim approached a bend. The car flew off the road at a tangent as he realised – too late – that he had been travelling at Caterham speeds into the corner and didn’t have a Caterham chassis, a Caterham suspension, or Caterham brakes. His old car was not up to dealing with the forces created in the turn. His tyres failed to grip and, after what seemed like an eternity of slow-motion spinning and screeching and panic, he find himself in a ditch; healthy, but with a wheel sheared off and smoke coming out of the front of the car. A later inspection confirmed that his commuting car was a write-off, and his insurance policy didn’t fully cover the cost of a new vehicle.

Jim ended up having to buy another day-to-day car, which delayed him from spending the additional money necessary to get the Caterham on the road for quite some time. However, after scrimping and saving for a while, he eventually got back to his dream project, only to find that combination of the modifications he had to make to the Duratec engine, plus the after effects of the crash meant that it was now useless and he needed to purchase a replacement.

So because Jim didn’t want to run to the expense of maintaining his old car while he built his new one, he would instead have to buy a new temporary car plus a new engine for the Caterham. Jim was just as far off from finishing the Caterham as when he had started, despite wasting a lot of time and money along the way. A very sad story.

+++

Suddenly I realised that I had been wittering on about a wholly unrelated subject to my friend’s data warehousing problem. I apologised and turned the conversation back to this. To my astonishment, she told me that she had already made up her mind. I suppose she had taken advantage of the length of time I had spent telling Jim’s story to more profitably weigh the pros and cons of different approaches in her mind and thereby had reached her decision. Anyway, she thanked me for my help, I protested that I hadn’t really offered her any and we each went our separate ways.

I found out later she had decided to pay the maintenance costs on the old data warehouse.


I would like to apologise in advance if anyone at Caterham, Cosworth, Ford, or indeed Peugeot, takes offence to any of the content of the above story or its illustrations. I’m sure that you make very fine products and this article isn’t really about any of them.


Filed under: data warehousing, project management Tagged: caterham, cosworth

Posted by Peter Thomas at 4:25 PM

March 2, 2011

I will be presenting at the IRM European Data Governance Conference

This IRM UK event will be taking place in central London from the 21st to 23rd March 2011. It is co-located with another related IRM conferences on Master Data Management.

My presentation will be entitled Making Business Intelligence an Integral part of your Data Quality Programme. Full details may be obtained from the IRM conference web-site here.
 


Filed under: general

Posted by Peter Thomas at 4:05 AM